The company is today launching what they dub a ‘video prediction engine’, which they claim is capable of detecting which videos will spread across the Web before they actually do.

PopScreen measures online videos from over 10,000 sources, including YouTube (duh), Vimeo and DailyMotion but also content networks like Revision3 and FunnyOrDie.

Its video prediction engine indexes around 15,000 videos per day based on those sources, and analyzes them in order to collect a number of unique data points – it’s unclear which ones exactly but the number runs in the ‘hundreds of thousands’ according to PopScreen – and processes them through a proprietary algorithm in an effort to accurately predict the viral potential of videos and to identify newsworthy content before your sister finds out about it.

Its main strength lies in filtering out duplicates, repeats and similar videos that are far less likely to scale up, tapping a network of influencers to determine which videos are most likely to go mainstream in the near future.

Part of the predictive process includes user input. When you sign up for a free account at PopScreen, you’re given the chance to install a bookmarklet in your browser to add videos to your own library on the site. Those results show up on the site’s PopCharts page.

PopScreen draws from dozens of sources, and even shows a leaderboard that lists how many videos are included from each site. As you’d expect YouTube tops the list with 673,166 videos at this writing.

The site’s home lets you choose between two views: Popular Now and On Our Radar, though the latter is not yet functional.

The brains behind PopScreen are Kevin Nguyen and Glenn Gutierrez, who have experience launching search engines that draw from other search sites. In 2003, they started ZapMeta, which returns results by searching other search engines.

How will PopScreen make money? Again, from TechCrunch:

PopScreen says its engine will be rolled out on a larger scale over the coming months to allow partners across all industries including news, publishing, and search, to syndicate predictive data and provide their users with access to the most timely video content on the Web, tailored speciﬁcally to their personal interests.

Can an algorithm predict what’s going to be hot on the Web? Predicting human likes and dislikes has never been easy for computers. Who would have thought, for example, that Squeaky the Herding Pig would have become a big hit?